الفهرس 
AbstractOnly 14 pages are availabe for public view
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Abstract
Wind energy generation is expected to increase in future electric grids. The generated wind power has an intermittent nature which may affect power system stability and increase the risk of blackouts. Therefore, a prediction system for wind power generation is essential for optimum operation of a power system with a significant share of wind energy conversion systems. An appropriate system is required to predict the output power from wind turbines (WT) immediately with acceptable accuracy and reliability. Forecasting WT power generation depends on the real parameters at WT such as (wind speed, temperature, pressure …). The important stage for reliable forecasting system is collecting real data from WT immediately. So, collecting these parameters values using appropriate method could be help forecast the output power of wind turbine immediately. Recently, Wireless Sensor Network (WSN) is most appropriate method used to collect data from any remote environment. So, reliable routing strategy for WSN is needed to monitor the wind turbine and forecast its output power immediately. WSN is used to measure object parameters then transmit the sensed data to center station called Sink Node (SN). Routing of the sensed data is a challenging issue since several parameters and restrictions should be managed carefully in WSN. Although the sensor’s power (e.g., sensor’s battery level) is a critical issue, managing data transmission time is also a considerable subject especially for real time applications. Several routing protocols had been proposed for WSN, however, each protocol considers a single type of awareness (such as; long life, delay time, total energy...).In this thesis, a multi-aware query driven (MAQD) routing protocol will be proposed for WSN. MAQD considers four types of awareness; the long life of the sensor, delay time of data transmission, total power cost of the network, and the shortest transmission path. Hence, based on the selected type of awareness, a node selects the proper path for routing data. MAQD is a query driven protocol, accordingly, SN can collect data from some/all sensors by employing a request (REQ) message in which the awareness type is specified. MAQD is simulated and tested using OPNET 14.5 and compared with the latest WSN routing protocols. Simulation results have shown that MAQD outperforms the selected competitors routing protocols (LEACH, ERTLD, RACE, SPIN, MM-SPEED, DCBM, Rumor and EAR2) as it introduces the best data delivery with the minimum routing overheads in terms of time penalties and power consumed. In this thesis aslo, a hybrid neuro-fuzzy wind power prediction model is proposed at operator center. The inputs to prediction model are parameters measured using A WSN. A WSN is used to measure and transmit the required parameters for the prediction model at the operator center using the proposed routing protocol. Those parameters are the major factors affecting wind farm output power, namely air temperature, wind speed, air density and air pressure. Considering all these factors will increase the prediction accuracy of the proposed model. The proposed prediction model is designed and tested using fuzzy rules with adaptive network. To decide the optimal number of fuzzy rules, the clustering of the data using modified Fuzzy C-Means (FCM) is used to implement hybrid optimization method. The prediction model is tested using four subsets of data divided into four seasons of year. The proposed prediction model is implemented using Matlab. The accuracy is tested and verified by calculating RMSE and ReErr are for four subsets. Analysis of results shows that the proposed model has average of the RMSE is 0.003743 and the average of ReErr is 5.86%.The proposed neuro-fuzzy model maintains good prediction accuracy and provides a useful qualitative description of the overall prediction system.